Camera model identification using local binary patterns

Guanshuo Xu, Yun Qing Shi

Research output: Contribution to journalConference articlepeer-review

58 Scopus citations


In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine learning and pattern recognition algorithms for similarity measures of extracted features. In this paper, we propose to use uniform gray-scale invariant local binary patterns (LBP) as statistical features. Considering 8-neighbor binary co-occurrence, three groups of 59 local binary patterns are extracted from the spatial domain of red and green color channels, their corresponding prediction-error arrays, and their 1st-level diagonal wavelet sub bands of each image, respectively. Multi-class support vector machines are built for classification of 18 camera models from âDresden Image Database'. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher.

Original languageEnglish (US)
Article number6298429
Pages (from-to)392-397
Number of pages6
JournalProceedings - IEEE International Conference on Multimedia and Expo
StatePublished - 2012
Event2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, Australia
Duration: Jul 9 2012Jul 13 2012

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications


  • LBP
  • camera model identification
  • local binary patterns
  • source device identification


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